Abstract

The aim of this paper is to provide a detailed description of a framework for the estimation of time-sliced origin-destination (OD) trip matrices in a transit network using counts and travel time data of Bluetooth Smartphone devices carried by passengers at equipped transit-stops. A Kalman filtering formulation defined by the authors has been included in the application. The definition of the input for building the space-state model is linked to network scenarios modeled with the transportation planning platform EMME. The transit assignment framework is optimal strategy-based, which determines the subset of paths related to the optimal strategies between all OD pair Document type: Part of book or chapter of book

Abstract

Part 3: Infrastructure Security International audience Historically, supervisory control and data acquisition (SCADA) systems have relied on obscurity to safeguard against attacks. Indeed, external attackers lacked knowledge about proprietary system designs and software to access systems and execute attacks. The trend to interconnect to the Internet and incorporate standardized protocols, however, has resulted in an increase in the attack surface – attackers can now target SCADA systems and proceed to impact the physical systems they control. Dynamical estimation can be used to identify anomalies and attempts to maliciously affect controlled physical systems. This paper describes an intrusion detection method based on the dynamical estimation of systems. A generic water pipeline system is modeled using state space equations, and a discrete-time Kalman filter is used to estimate operational characteristics for anomaly-based intrusion detection. The effectiveness of the method is evaluated against deception attacks that target the water pipeline system. A co-simulation that integrates computational fluid dynamics software and MATLAB/Simulink is employed to simulate attacks and develop detection schemes. Document type: Part of book or chapter of book

Abstract

This paper proposes an on-board Electric Vehicle (EV) battery charger with enhanced Vehicle-to-Home (V2H) operation mode. For such purpose was adapted an on-board bidirectional battery charger prototype to allow the Grid-to-Vehicle (G2V), Vehicle-to-Grid (V2G) and V2H operation modes. Along the paper are presented the hardware topology and the control algorithms of this battery charger. The idea underlying to this paper is the operation of the on-board bidirectional battery charger as an energy backup system when occurs a power outages. For detecting the power outage were compared two strategies, one based on the half-cycle rms calculation of the power grid voltage, and another in the determination of the rms value based in a Kalman filter. The experimental results were obtained considering the on-board EV battery charger under the G2V, V2G, and V2H operation modes. The results show that the power outage detection is faster using a Kalman filter, up to 90% than the other strategy. This also enables a faster transition between operation modes when a power outage occurs. Fundação para a Ciência e Tecnologia (FCT), Project Scope: Pest OE/EEI/UI0319/201

Abstract

The modern-day vehicle is evolved in a cyber-physical system with internal networks (controller area network (CAN), Ethernet, etc.) connecting hundreds of micro-controllers. From the traditional core vehicle functions, such as vehicle controls, infotainment, and power-train management, to the latest developments, such as advanced driver assistance systems (ADAS) and automated driving features, each one of them uses CAN as their communication network backbone. Automated driving and ADAS features rely on data transferred over the CAN network from multiple sensors mounted on the vehicle. Verifying the integrity of the sensor data is essential for the safety and security of occupants and the proper functionality of these applications. Though the CAN interface ensures reliable data transfer, it lacks basic security features, including message authentication, which makes it vulnerable to a wide array of attacks, including spoofing, replay, DoS, etc. Using traditional cryptography-based methods to verify the integrity of data transmitted over CAN interfaces is expected to increase the computational complexity, latency, and overall cost of the system. In this paper, we propose a light-weight alternative to verify the sensor data’s integrity for vehicle applications that use CAN networks for data transfers. To this end, a framework for 2-dimensional quantization index modulation (2D QIM)-based data hiding is proposed to achieve this goal. Using a typical radar sensor data transmission scenario in an autonomous vehicle application, we analyzed the performance of the proposed framework regarding detecting and localizing the sensor data tampering. The effects of embedding-induced distortion on the applications using the radar data were studied through a sensor fusion algorithm. It was observed that the proposed framework offers the much-needed data integrity verification without compromising on the quality of sensor fusion data and is implemented with low overall design complexity. This proposed framework can also be used on any physical network interface other than CAN, and it offers traceability to in-vehicle data beyond the scope of the in-vehicle applications. Document type: Article